code: def test_sparse_inputs():
      X = csr_matrix((3, 4))
      y = np.asarray([0, 0, 1], dtype=np.int32)
  
      base = SGDClassifier(tol=1e-3)
      base = base.fit(X, y)
  
      wrap = ParallelPostFit(base)
      X_da = da.from_array(X, chunks=(1, 4))
  
      result = wrap.predict(X_da).compute()
      expected = base.predict(X)
  
      assert_eq_ar(result, expected)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 12 for calling a global function with example usage: test_sparse_inputs() without return types